Word Sense Induction
19 papers with code • 1 benchmarks • 1 datasets
Word sense induction (WSI) is widely known as the “unsupervised version” of WSD. The problem states as: Given a target word (e.g., “cold”) and a collection of sentences (e.g., “I caught a cold”, “The weather is cold”) that use the word, cluster the sentences according to their different senses/meanings. We do not need to know the sense/meaning of each cluster, but sentences inside a cluster should have used the target words with the same sense.
Description from NLP Progress
Benchmarks
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Latest papers with no code
The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks
The repository reflects the task's modularity by allowing model evaluation for WiC, WSI and LSCD.
Word Sense Induction with Knowledge Distillation from BERT
This paper proposes a two-stage method to distill multiple word senses from a pre-trained language model (BERT) by using attention over the senses of a word in a context and transferring this sense information to fit multi-sense embeddings in a skip-gram-like framework.
Word Sense Induction with Hierarchical Clustering and Mutual Information Maximization
In this paper, we propose a novel unsupervised method based on hierarchical clustering and invariant information clustering (IIC).
Towards Automatic Construction of Filipino WordNet: Word Sense Induction and Synset Induction Using Sentence Embeddings
The resulting sense inventory and synonym sets can be used in automatically creating a wordnet.
Topological Data Analysis for Word Sense Disambiguation
We develop and test a novel unsupervised algorithm for word sense induction and disambiguation which uses topological data analysis.
Large Scale Substitution-based Word Sense Induction
We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora.
BOS at SemEval-2020 Task 1: Word Sense Induction via Lexical Substitution for Lexical Semantic Change Detection
The first solution performs word sense induction (WSI) first, then makes the decision based on the induced word senses.
Topology of Word Embeddings: Singularities Reflect Polysemy
We argue that we should, more accurately, expect them to live on a pinched manifold: a singular quotient of a manifold obtained by identifying some of its points.
Combining Neural Language Models for WordSense Induction
Word sense induction (WSI) is the problem of grouping occurrences of an ambiguous word according to the expressed sense of this word.
A Comparative Study of Lexical Substitution Approaches based on Neural Language Models
Lexical substitution in context is an extremely powerful technology that can be used as a backbone of various NLP applications, such as word sense induction, lexical relation extraction, data augmentation, etc.
